59 citations · 443 across the 46 of their papers we have counts for
7 papers · 1 filter
Supervised learning of analysis-sparsity priors with automatic differentiation
Hashem Ghanem, Joseph Salmon, Nicolas Keriven +1
Sparsity priors are commonly used in denoising and image reconstruction. For analysis-type priors, a dictionary defines a representation of signals that is likely to be sparse. In…
Graph-Theoretic Models of Resource Distribution for Cyber-Physical Systems of Disaster-Affected Regions
Kenneth Johnson, Samaneh Madanian, Roopak Sinha
We propose a tool-supported framework to reason about requirements constraining resource distributions and devise strategies for routing essential services in a disaster-affected r…
LassoBench: A High-Dimensional Hyperparameter Optimization Benchmark Suite for Lasso
Kenan Šehić, Alexandre Gramfort, Joseph Salmon +1
While Weighted Lasso sparse regression has appealing statistical guarantees that would entail a major real-world impact in finance, genomics, and brain imaging applications, it is…
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
Paul Mangold, Aurélien Bellet, Joseph Salmon +1
Machine learning models can leak information about the data used to train them. To mitigate this issue, Differentially Private (DP) variants of optimization algorithms like Stochas…
Score-Based Change Detection for Gradient-Based Learning Machines
Lang Liu, Joseph Salmon, Zaid Harchaoui
The widespread use of machine learning algorithms calls for automatic change detection algorithms to monitor their behavior over time. As a machine learning algorithm learns from a…
Spatially relaxed inference on high-dimensional linear models
Jérôme-Alexis Chevalier, Tuan-Binh Nguyen, Bertrand Thirion +1
We consider the inference problem for high-dimensional linear models, when covariates have an underlying spatial organization reflected in their correlation. A typical example of s…